Augmenting user responses to queries

ABSTRACT

Generating a query response by receiving data for a non-user utterance, determining a question answering (QA) system response to the non-user utterance, receiving data for a user utterance responsive to the non-user utterance, determining a confidence score for the user utterance, determining a deviation between the user utterance and the QA system response, and providing the QA system response according to a combination of the deviation and the confidence score.

FIELD OF THE INVENTION

The disclosure relates generally to the generating responses to queries.The disclosure relates particularly to automated generation of responsesto augment user response to queries.

BACKGROUND

Wearable devices exist to provide data indicative of a user's currenthealth. Such data includes, a user's heart rate, blood pressure, bloodoxygen level, blood glucose level, and other user health condition data.Such data may be collected and tracked over time, providing anindication of changes in a user's medical condition.

Speech to text algorithms provide a means for converting digitized audiofiles to text files. Text to speech algorithms provide a means forconverting digital text data to digitized audio data which can be playedas audio from a speaker.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the disclosure. This summary is not intended toidentify key or critical elements or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, devices, systems, computer-implemented methods,apparatuses and/or computer program products enable generating responsesto queries.

Aspects of the invention disclose methods, systems and computer readablemedia associated with generating a query response by receiving data fora non-user utterance, determining a question answering (QA) systemresponse to the non-user utterance, receiving data for a user utteranceresponsive to the non-user utterance, determining a confidence score forthe user utterance, determining a deviation between the user utteranceand the QA system response, and providing the QA system responseaccording to a combination of the deviation and the confidence score.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the more detailed description of some embodiments of the presentdisclosure in the accompanying drawings, the above and other objects,features and advantages of the present disclosure will become moreapparent, wherein the same reference generally refers to the samecomponents in the embodiments of the present disclosure.

FIG. 1 provides a schematic illustration of a computing environment,according to an embodiment of the invention.

FIG. 2 provides a flowchart depicting an operational sequence, accordingto an embodiment of the invention.

FIG. 3 depicts a cloud computing environment, according to an embodimentof the invention.

FIG. 4 depicts abstraction model layers, according to an embodiment ofthe invention.

DETAILED DESCRIPTION

Some embodiments will be described in more detail with reference to theaccompanying drawings, in which the embodiments of the presentdisclosure have been illustrated. However, the present disclosure can beimplemented in various manners, and thus should not be construed to belimited to the embodiments disclosed herein.

In making a diagnosis and preparing a treatment plan according to thatdiagnosis, medical practitioners rely upon information received from thepatient. Inaccuracies in the patient provided information may lead to amisdiagnosis and an incorrect treatment plan. The result of this mayinclude incomplete or delayed patient recovery, or a complete lack ofpatient recovery from the current medical state. Patient's answer apractitioner's queries to the best of their ability but may not alwayspresent accurate response for a variety of reasons. Patients may notremember accurate answers or may not have been tracking their symptomsclosely enough to provide accurate answers to queries such as “How manydays have you had a fever?”, or “How long have you had your currentsymptoms?”. Disclosed embodiments combine data from wearable biometricsensors, image analysis and real-time patient-practitioner conversationanalysis to determine that a patient lacks confidence in their answer toa query and that analysis of the patient's biometric data leads to adifferent answer.

Aspects of the present invention relate generally to question answeringsystems and, more particularly, to unsupervised dynamic confidencethresholding for answering questions. In embodiments, a questionanswering (QA) system receives a question from a user device, determinesone or more answers for the question, returns the determined answerswhose confidence score is greater than a confidence threshold, and doesnot return the determined answers whose confidence score is less thanthe confidence threshold. The system further receives a user response tothe questions together with data indicative of the user's confidence intheir own answer. The system analyzes the user response data todetermine the user's confidence in the answer and compares the user'sanswer to answers generated by the system in response to the samequestion. According to aspects of the invention, in instances where theuser's confidence is low, there is a difference between the user'sanswer and at least one answer generated by the system, and the user hasopted in to automatic responses, the QA system automatically providesthat system answer as an alternative to the user's answer. In thismanner, implementations of the invention provide alternative answersoffering higher levels of accuracy in response to queries posed to theuser. System responses may be provided automatically under theseconditions depending upon the opt-in status of the user. When the userhas opted-in to automatic responses and when the system determines thatthe current query is non-sensitive in nature, the system automaticallyprovides the generated response. For sensitive queries, the systemprovides the alternative answer to the user enabling the user to sharethe alternative answer with the originator of the query.

In accordance with aspects of the invention there is a method forautomatically generating responses to queries made to a user.Embodiments convert a query to digital text data using speech to textprogramming and then process the digitized text suing a Natural languageprocessing (NLP) algorithm to determine one or more intents includedwithin the query. Embodiments provide the NLP output as an input to a QAsystem including a QA corpus. The QA system determines one or moreresponses to the input, each response includes an associated confidencelevel corresponding to the likelihood that the particular responsesselected is the best response to the intent expressed in the query asembodied in the output of the NLP algorithm.

Aspects of the invention provide an improvement in the technical fieldof QA systems. Conventional QA systems utilize static matching databasesor defined decision trees to determine an answer to a question posed toa user. In many cases, the predefined answers lack up to dateinformation resulting in responses of little actual value to thequestioner. Further, users in some settings, such as being questioned bya medical professional, may feel stressed and have difficultyinterpreting questions and/or providing accurate responses. In someinstances, a patient may be unable to provide answers due to the veryconditions leading to efforts to seek treatment. Implementations of theinvention leverage patient medical data provided by wearable sensorssuch that disclosed systems and methods provide the improvement offormulating accurate responses to health care provider questions,thereby enabling the desired user outcome of an accurate diagnosis andeffective therapies for the patient/user.

Aspects of the invention also provide an improvement to computerfunctionality. In particular, implementations of the invention aredirected to a specific improvement to the way QA systems operate,embodied in the continually adjusted patient medical data and history aswell as monitoring a patient/user's confidence in a provided answer aswell as differences between patient answers and system answers derivedfrom up to date health data. In embodiments, the system adjusts thehealth profile of the user according to continuously captured biometrichealth data and monitors the user's responses to questions. Both theintent of the response and the user's confidence in the response aretracked and used in determining a system action. As a result ofadjusting a user's health profile and monitoring user responses andassociated confidence levels, the system increases the likelihood thatthe system and user will provide accurate answers to health questions insituations where user answers fail to provide accurate information to aquestioner. In this manner, embodiments of the invention affect how theQA system functions (i.e., the likelihood of providing up to date andaccurate answers to queries when a user fails to do so.

As an overview, a QA system is an artificial intelligence applicationexecuted on data processing hardware that answers questions pertainingto a given subject-matter domain presented in natural language. The QAsystem receives inputs from various sources including input over anetwork, a corpus of electronic documents or other data, data from acontent creator, information from one or more content users, health datafrom one or more wearable sensors, user data from one or more camerasand/or microphones, and other such inputs from other possible sources ofinput. Collected data may further include textual health data of theuser including named entity recognition data extracted using naturallanguage understanding algorithms. Data may also include environmentaldata such as user's locations, and temperature, barometric pressure, airquality, humidity, etc., of the user's surroundings. Environmental datamay further include the user's proximity to contagious individualsidentified anonymously by the system as other system participants, orthe location of the user in or near an afflicted area having a knownhigh rate of incidence of a particular disease or conditions associatedwith particular diseases. Data may also include time durations which theuser has spent in close proximity to afflicted individuals. Data mayfurther user food intake data provided by a user through a userinterface to the system resident upon the user's device such as acomputer, tablet computer or smartphone. Data storage devices store thecorpus of data. A content creator creates content in a document for useas part of a corpus of data with the QA system. The document may includeany file, text, article, or source of data for use in the QA system. Forexample, a QA system accesses a body of knowledge about the domain, orsubject matter area (e.g., financial domain, medical domain, legaldomain, etc.) where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, such as but not limited to astructured repository of domain-specific information, such asontologies, or unstructured data related to the domain, or a collectionof natural language documents about the domain.

In an embodiment, the method standardizes and normalizes the receiveduser data from wearable sensors and other sources described above. Thestandardized and normalized data is then ingested by the system andanalyzed to extract a body of useful information related to the currentdomain of the method (e.g., health information queries). The systemstores the data and augments the stored body of data as the systemreceives additional user data. The system utilizes the stored data tobuild a knowledge graph, decision tree, matching database or other QAdecision structure incorporating the data, for generating responses toqueries.

Disclosed embodiments are discussed in terms of a conversation between auser and health care provider as a non-limiting example. Disclosedembodiments may also be applicable to conversations between a user andany service provider by appropriately expanding the corpus of the systemto include such things as a user's financial records or private data.

In an embodiment, one or more components of the system can employhardware and/or software to solve problems that are highly technical innature (e.g., receiving data associated with a non-user utterance,determining an intent of the non-user utterance using natural languageprocessing or similar methods, determining a system response to thenon-user utterance according to the intent, receiving data associatedwith a user response to the non-user utterance, determining a confidencescore associated with the user response, determining a deviation betweenthe user response and the system response, providing the system responseaccording to the deviation between the user response and the systemresponse and the confidence level, etc.). These solutions are notabstract and cannot be performed as a set of mental acts by a human dueto the processing capabilities needed to facilitate augmenting userquery responses, for example. Further, some of the processes performedmay be performed by a specialized computer for carrying out definedtasks related to providing responses to queries. For example, aspecialized computer can be employed to carry out tasks related toassisting a user in responding to a query or the like.

In an embodiment, a method for augmenting a user's responses to queriesincludes receiving data associated with or corresponding to a non-userutterance. The non-user may have asked the user a question, eitherorally, or in a written form, such as via an SMS text messagecommunicated over a telecommunications network. In this embodiment, themethod receives the non-user utterance data and processes the data.Processing of oral communications data includes speech-to-textprocessing. The method translates a digital audio data file derived fromthe oral communication to a string of phonemes corresponding to thespoken words of the original speech. The speech-to-text algorithm thentranslates the string of phonemes to a string of words, the digitizedtext output of the algorithm.

In an embodiment, the method passes the text derived from the oralcommunications, or the text received directly, to a natural languageprocessing (NLP) algorithm to determine one or more intents associatedwith the text.

In an embodiment, the method further analyses the audio data for thenon-user utterance to derive a non-user confidence level according to anattribute such as tonal analysis of the data. In an embodiment, themethod further analyzes the non-user utterance using attributesincluding pause gap analysis, personality trait analysis, and facialexpression analysis of images of the non-user during the utterance andongoing conversation with the user. These analyses provides anindication of the confidence of the non-user in their utterance. Forexample, the confidence of a health care provider in their diagnosis. Inthis embodiment, the method uses a function combining the attributessuch as tonal analysis output, the facial expression output, personalitytrait analysis output, and the pause gap analysis combined withweighting coefficients for each of the values, to determine an overallconfidence value for the utterance. In an embodiment, the method uses along short-term memory neural network (LSTM), or similar architecture,to classify the confidence value for the utterance. In training the LSTMmodel, the method initializes the weights using random values. Themethod then adjusts the weights using backpropagation and labeled dataderived from interactions between the non-user and the QA system. Overtime and the course of a plurality of interactions, the weights aretrained and calibrated to fit the personality traits and communicationstyle of the non-user.

Disclosed embodiments can perform natural language processing (NLP) forextraction of NLP output parameter values from received voice data ofuser, as well as prompting data from a VA. Embodiments may perform oneor more of: a topic classification process that determines topics ofmessages and outputs one or more topic NLP output parameter value, asentiment analysis process which determines sentiment parameter valuefor a message, e.g., polar sentiment NLP output parameters, “negative,”“positive,” and/or non-polar NLP output sentiment parameters, e.g.,“anger,” “disgust,” “fear,” “joy,” and/or “sadness” or otherclassification process for output of one or more other NLP outputparameter values, e.g., one or more “social tendency” NLP outputparameter, related to personality traits of a speaker, or one or more“writing style” NLP output parameter, and/or one or more part of speechNLP output parameter value. Part-of-speech tagging methodologies caninclude use of, e.g., Constraint Grammar, Brill tagger, Baum-Welchalgorithm (the forward-backward algorithm) and the Viterbi algorithmwhich can employ use of the Hidden Markov models. Hidden Markov modelscan be implemented using the Viterbi algorithm. The Brill tagger canlearn a set of rule patterns and can apply those patterns rather thanoptimizing a statistical quantity. Applying natural language processingcan also include performing sentence segmentation which can includedetermining where a sentence ends, including, e.g., searching forperiods, while accounting for periods that designate abbreviations.

Disclosed embodiments performing natural language processing can includeperforming (a) topic classification and output of one or more topic NLPoutput parameter for a received message, (b) sentiment classificationand output of one or more sentiment NLP output parameter value for areceived message, or (c) other NLP classifications and output of one ormore other NLP output parameter for the received message. Topic analysisfor topic classification and output of NLP output parameter values caninclude topic segmentation to identify several topics within a message.Topic analysis can apply a variety of technologies, e.g., one or more ofhidden Markov model (HMM), artificial chains, passage similarities usingword co-occurrence, topic modeling, or clustering. Sentiment analysisfor sentiment classification and output of one or more sentiment NLPparameter can determine the attitude of a speaker or a writer withrespect to some topic or the overall contextual polarity of a document.The attitude may be the author's judgment or evaluation, affective state(the emotional state of the author when writing), or the intendedemotional communication (emotional effect the author wishes to have onthe reader). In one embodiment, sentiment analysis can classify thepolarity of a given text as to whether an expressed opinion is positive,negative, or neutral. Advanced sentiment classification can classifybeyond a polarity of a given text. Advanced sentiment classification canclassify emotional states as sentiment classifications. Sentimentclassifications can include the classification of “anger,” “disgust,”“fear,” “joy,” and “sadness.” Such classification results include avector associated with the non-user utterance and a confidence valueassociated with each classification.

In this embodiment, the method provides the output of the NLP as aninput to a question answering (QA) system. The QA system includes amatching database, decision tree, or knowledge graph, constructed usinga provided corpus of knowledge relating to the specific domain thesystem and method are directed toward. For example, financialinformation, medical and/or health information, etc. For the example ofhealth and medical applications, the corpus includes biometric healthdata collected from one or more wearable sensors worn by the currentsystem user. Exemplary wearable sensors include blood pressure monitors,heart rate and blood oxygen monitors, blood glucose monitors, bodytemperature, etc. For this example, the method and system track wearablegenerated data over time and monitor trends in the provided data as wellas deviations from normal over the monitoring time period. In thisembodiment, the system and method track data in comparison to normal forthe user as well as normal in a broader sense, such as normal for anadult male, aged fifty-five. In this embodiment, the method maintainsand updates a database of user data as well as a database of normal datavalues for demographic groups relevant to the user. Such data bases maybe maintained on a local computing device for the user or may bemaintained on edge cloud or cloud resources to provide additionalstorage and computing resources.

In an embodiment, the method analyzes user health data from thewearable(s) and predicts non-user queries according to the currenttrends in the health data. In this embodiment, the method utilizesavailable medical diagnosis expert systems in the evaluation of thehealth data. The method determines a predicted diagnosis of the user'scurrent state and generates precited questions associated with thepredicted diagnosis, as well as an order of progression for the set ofpredicted questions. For each predicted diagnosis, the method generatesa confidence score based upon the likelihood that the predicteddiagnosis is accurate. As an example, the method analyzes the healthdata using a machine learning classification model and determines aconfidence associated with respective differing diagnosis arising theclassification based upon the health data. In this embodiment, themethod uses a machine learning classification model such as aconvolutional neural network, a recurrent neural network, a generativeadversarial network, a variational autoencoder, a long short-term memorymodel, or other classification models to analyze the health data of theuser. In an embodiment, training such a model includes providing themodel with labeled health data associated with known diagnoses and usingback propagation to derive appropriate network node weights for themodel. The method dynamically generates response to the predictedquestion using the QA system and the current health data and datatrends. In one embodiment, the system collects additional health dataaccording to a predicted diagnosis. The system collects additionalhealth data to corroborate or refute the predicted diagnosis.

In an embodiment, the user opts-in to social media analysis by thesystem and provides data relevant for system access to the user's socialmedia accounts. In this embodiment, the system analyzes the social mediaaccounts of the user to evaluate the user's mental health and currentphysical activities. Further analysis provides the method withindications as to the personality traits and normal communicationsstyles of the user in varying situations. These indications serve asinputs in determining the user's confidence is their responses toqueries.

In an embodiment, the method analyzes images gathered from system oruser device cameras. The analysis included comparisons in images takenover time to evaluate changes associated with aberrations in a user'sappearance or other changes, such as a wound healing or a rash changing.In an embodiment, the analysis further includes facial expressionanalysis of the user during conversations. Facial expression analysisserves as an input to confidence score determinations.

In an embodiment, the method provides the most recent intent derived bythe NLP from the non-user utterance data to the QA system. In anembodiment, the method provides the on-going string of intentsassociated with non-user utterances from a conversation between the userand the non-user to the QA system. In this embodiment, the QA systemanalyzes the intent or series of intents and determines an outputresponse to the most recent intent, either alone or in the context ofthe string of intents from the conversation. In one embodiment, theresponse includes health data relevant to the input intent and theresponse. The response output from the QA system includes a vectorrepresentation of the response. The QA system provides a confidencescore for one or more responses to the query, either as part of thevector representation of the response or as a separate value.

In an embodiment, the system utilizes Lambda processing architectureprocessing historic health data and health information to generate adiagnosis and predict questions for a health care provider as a batchprocess, then uses a speed layer process to evaluate real-time non-userqueries and user responses using the developed QA response generatingsystem.

The method and system monitor the user and effectively listen orotherwise receive the user's response to the non-user utterance. Thesystem may utilize a microphone of a user's device or system computingdevice to capture audio of a user response, or the system may monitor anelectronic text conversation between the user and on-user to capture theuser's text-based response to a text-based non-user query.

Similarly, to the non-user utterance, the method and system process userresponse audio data using speech-to-text to yield digitized text datafrom the user audio or translate a user's text response to digitizedtext data. In an embodiment, the method processes the user response datausing NLP to generate a vector representation of the user's response. Inan embodiment, the method and system utilize tonal analysis, pause gapanalysis, personality trait analysis, and facial expression analysis, ofuser audio data and image data, to determine a user confidence value forthe user's response. The confidence value represents the level ofconfidence of the user that the user provided response is accurate andcomplete.

In this embodiment, the method uses a function combining the tonalanalysis output, the facial expression output, personality analysis, andthe pause gap analysis combined with weighting coefficients for each ofthe values, to determine an overall confidence value for the utterance.In an embodiment, the method uses a long short-term memory neuralnetwork (LSTM), or similar architecture, to classify the confidencevalue for the utterance. In training the LSTM model, the methodinitializes the weights using random values. The method then adjusts theweights using backpropagation and labeled data derived from interactionsbetween the user and the QA system and optionally labeled data from theuser's social media and other communication outlets. Over time and thecourse of a plurality of interactions, the weights are trained andcalibrated to fit the personality traits and communication style of theuser.

In an embodiment, the method compares the vector associated with thesystem's generated response to the non-user query with the vector forthe user's response to the same query. In this embodiment, the methoddetermines a similarity and deviation between the two vectors. Examplesof methods of determining the similarity of text-based documents includeJaccard distance, Cosine distance, Euclidean distance, Relaxed WordMover's Distance, and may utilize term frequency-inverse documentfrequency (tf-idf) techniques. A person of ordinary skill in the art mayapply other techniques of determining similarity between page pairingsof a document other than those presented, herein, and not deviate from,or limit the features of embodiments of the present invention.

In an embodiment, the method uses a combination of the user's confidencescore and the degree of deviation between the response vectors todetermine the method's action relating to the generated response. When adeviation between the response vectors plus the inverse of the score ofuser confidence exceeds a defined threshold, the method next considersuser preferences regarding the next action. The method considers theinverse of the user's confidence score in combining the factors suchthat a low user confidence increases the likelihood of a systeminterjection while a high user confidence decreases the likelihood of aninterjection.

A user may opt-in to automated responses wherein the system interjectsthe generated response into the ongoing conversation. In an embodiment,the system asks for confirmation from the user before proceeding tointerject. A user may choose to opt-out of automatic interjection ofresponses. In this case the system detects that the combination of userconfidence and response vector deviation exceeds the defined thresholdand may then provide the user with the generated response, enabling theuser to choose whether or not to provide the generated response. In thisembodiment, the system may provide the generated response to the userusing a text message to the user's device such as a smartphone. In anembodiment, the system may provide the user an option to enable thesystem to continue the conversation with the non-user based upongenerated responses derived using the biometric data embedded in theknowledge graph of the QA system.

In an embodiment, the method further considers the non-user confidenceas well as the user confidence and the response deviation in determiningthe system's next action. As an example, the method may reduce aweighting associated with the response deviation for instances where thepredicted diagnosis differs from non-suer's diagnosis and the non-user'sconfidence level is high, e.g., exceeding 90%. In an embodiment, themethod compares the non-user confidence score and the confidence scoreassociated with the generated system response and system diagnosis indetermining the next system action. System confidence scores higher thannon-user confidence scores weigh toward providing the system generatedresponse while non-user confidence scores higher than system generatedresponse confidence scores weigh toward not providing the generatedresponse to the user.

In an embodiment, the system determines that the combination of userconfidence and response vector deviation exceeds the defined thresholdand offers the user a set of recommendations/actions such as: suggestingthe user provide the generated response to the non-user, suggesting thatthe system converse with the non-user to provide the generated response,suggesting the system take over the conversation and provide thegenerated response as well as additional supporting data to thenon-user.

In an embodiment, the method considers the context of the communicationin determining a next system action. For example, the method considersthe nature of the conversation—e.g., in-person, telemedicine visit,text-only conversation, etc., in determining whether the system shouldinterject directly or provide generated response to the user.

In an embodiment, the method considers the sensitivity of the non-userquery in determining a next system action. For queries relating tosensitive health matters, the system may provide the generated responseto the user rather than interjecting the response directly into theconversation. In this embodiment, the method avoids providing audibleresponses which include potentially sensitive user health information,which responses may be overheard. In this embodiment, the method usesthe NLP generated vectors for the non-user utterance, the systemgenerated response and the user response utterance, in determining querysensitivity.

In an embodiment, the method provides the generated responses using textto speech programming wherein the generated responses are provided as asynthesized string of phonemes enabling an audible response from thesystem when enabled by the user. In this embodiment, the method providesthe text-to-speech data to the user's device such as a smartphone.

FIG. 1 provides a schematic illustration of exemplary network resourcesassociated with practicing the disclosed inventions. The inventions maybe practiced in the processors of any of the disclosed elements whichprocess an instruction stream. As shown in the figure, a networkedClient device 110 connects wirelessly to server sub-system 102. Clientdevice 104 connects wirelessly to server sub-system 102 via network 114.Client devices 104 and 110 comprise response generation program (notshown) together with sufficient computing resource (processor, memory,network communications hardware) to execute the program. In anembodiment, client devices 104 and 110 constitute user access points forthe response generation program, providing user interfaces to enablereceipt of utterance data and the provision of generated responseoutputs to the user. As shown in FIG. 1 , server sub-system 102comprises a server computer 150. FIG. 1 depicts a block diagram ofcomponents of server computer 150 within a networked computer system1000, in accordance with an embodiment of the present invention. Itshould be appreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments can be implemented. Manymodifications to the depicted environment can be made.

Server computer 150 can include processor(s) 154, memory 158, persistentstorage 170, communications unit 152, input/output (I/O) interface(s)156 and communications fabric 140. Communications fabric 140 providescommunications between cache 162, memory 158, persistent storage 170,communications unit 152, and input/output (I/O) interface(s) 156.Communications fabric 140 can be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications, and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system. For example, communications fabric 140 can beimplemented with one or more buses.

Memory 158 and persistent storage 170 are computer readable storagemedia. In this embodiment, memory 158 includes random access memory(RAM) 160. In general, memory 158 can include any suitable volatile ornon-volatile computer readable storage media. Cache 162 is a fast memorythat enhances the performance of processor(s) 154 by holding recentlyaccessed data, and data near recently accessed data, from memory 158.

Program instructions and data used to practice embodiments of thepresent invention, e.g., the response generating program 175, are storedin persistent storage 170 for execution and/or access by one or more ofthe respective processor(s) 154 of server computer 150 via cache 162. Inthis embodiment, persistent storage 170 includes a magnetic hard diskdrive. Alternatively, or in addition to a magnetic hard disk drive,persistent storage 170 can include a solid-state hard drive, asemiconductor storage device, a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM), a flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 170 may also be removable. Forexample, a removable hard drive may be used for persistent storage 170.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage170.

Communications unit 152, in these examples, provides for communicationswith other data processing systems or devices, including resources ofclient computing devices 104, and 110. In these examples, communicationsunit 152 includes one or more network interface cards. Communicationsunit 152 may provide communications through the use of either or bothphysical and wireless communications links. Software distributionprograms, and other programs and data used for implementation of thepresent invention, may be downloaded to persistent storage 170 of servercomputer 150 through communications unit 152.

I/O interface(s) 156 allows for input and output of data with otherdevices that may be connected to server computer 150. For example, I/Ointerface(s) 156 may provide a connection to external device(s) 190 suchas a keyboard, a keypad, a touch screen, a microphone, a digital camera,and/or some other suitable input device. External device(s) 190 can alsoinclude portable computer readable storage media such as, for example,thumb drives, portable optical or magnetic disks, and memory cards.Software and data used to practice embodiments of the present invention,e.g., response generating program 175 on server computer 150, can bestored on such portable computer readable storage media and can beloaded onto persistent storage 170 via I/O interface(s) 156. I/Ointerface(s) 156 also connect to a display 180.

Display 180 provides a mechanism to display data to a user and may be,for example, a computer monitor. Display 180 can also function as atouch screen, such as a display of a tablet computer.

FIG. 2 provides a flowchart 200, illustrating exemplary activitiesassociated with the practice of the disclosure. After program start, atblock 210, response generating program 175, receives data associatedwith a non-user utterance. The data may include audio data, text data,or a combination of audio and text data. In an embodiment, the methodprocesses audio data using speech to text programming. The methodprocesses the data suing NLP programming yielding one or more intentsfrom the data. In an embodiment, the method further processes the audiodata to determine a non-user confidence score for the non-user utteranceusing tonal analysis, pause gap analysis. In an embodiment, the methodanalyzes images of the non-user during the conversation using facialexpression analysis to support the confidence score determination.

At block 220, the method of response generating program 175 determines aresponse to the non-user utterance using a QA system and knowledgecorpus embodied in a decision tree, knowledge graph, or other QAstructure. In an embodiment, the QA structure includes decisions nodesor comparable structures associated with biometric data received fromone or more wearable sensors. The method receives the intent of thenon-user utterance from the NLP and processes the intent—or string ofrecent intents—to determine the system response to the non-userutterance. In an embodiment, the method processes the generated responseusing NLP and determines a vector representation and confidence scorefor the generated response.

At block 230, the method of response generating program 175 receivesdata associated with a user response to the non-user utterance. Thisdata may be audio data, text data, or a combination of audio and textdata. For audio data the system and method use speech-to-text processingto convert the audio data to text data for further processing. Themethod processes the text data using NLP to generate a vectorrepresentation of the user response data.

At block 240, the method of response generating program 175 furtherprocesses data, including user response audio data and user image data,to determine a user confidence score for the user response. Theconfidence score includes tonal analysis, pause gap analysis of the userresponse, personality trait analysis, and facial cue analysis of theuser images captured during the response. In an embodiment, the methoduses a trained LSTM model to determine the user confidence score fromthe audio and image data.

At block 250, the method determines a deviation between the vectorrepresentation of the system response and the vector representation ofthe user response. The method may use cosine similarity or similarmethods to determine the deviation between the vectors.

At block 260, the method determines a system action to be taken, such asproviding the system response to the non-user utterance. The systemcombines the user confidence score and the response vector deviation todetermine if the system response should be provided to the user,interjected into the conversation, or both. The user may opt-in tohaving the system automatically interject the system response when theuser confidence is low and the vector deviation is high, or to have thesystem prompt the user for guidance in such circumstances. The user mayenable the system to take over the conversation with the non-user undersuch circumstances, enabling the system to provide the non-user withadditional information subject to non-user queries, or subject to adetermination of relevance of the additional information by the system.The system considers the user's opt-in status in determining a nextaction to take.

In an exemplary embodiment, a user registers for the disclosed serviceand receives a unique user identification. The user account isregistered and all information collected from that point in time will bestored in a private cloud associated with the user's account andidentification. The user may opt-in to allowing the system access to thecollected information. The user's wearable sensor(s) are associated withthe account and the method captures all data gathered by the sensor(s)and stores the data in the private cloud. In an embodiment, the methodstandardizes, normalizes and stores the incoming user data, includingbiometric sensor data, health history data, etc., the method extractsinformation from the data and generates a knowledge graph, decision treeor other QA system architecture from the data.

In this embodiment, a user begins to experience a low-grade fever on aThursday, as recorded by the system tracking data from a bodytemperature sensor worn by the user. The user is unaware of theircondition. The fever persists through the weekend. By Monday the user'scondition has worsened, leading them to schedule an appointment withtheir physician. At the physician's office on Monday afternoon, theirDoctor asks how long they have been feeling ill.

The system processes the Doctor's question and the QA portion of thesystem generates answers including “I have had a low-grade fever sincelast Thursday” from the NLP intent extracted from the Doctor's questionand the tracked biometric data from the wearable sensor. The userresponds, “I started to feel sick yesterday.” The system processes theuser's response using NLP and determines the user's confidence score forthe response from tonal analysis, pause gap analysis and facialexpression analysis combined with a user personality trait analysis. Thedetermined confidence score is high as the user is unaware of the timingof the onset and the duration of their symptoms. The system determines alarge deviation between the user's response and the system generatedanswer supported by the biometric data from the wearable sensor. Thesystem prompts the user with the generated response “I have had alow-grade fever since last Thursday” and enables the user to choosewhether to provide their Doctor with this response according to theopt-in status of the user. The user provides the generated response andopts-in to allowing the system to share data directly with the Doctor.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 3 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 3 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 4 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 3 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 4 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture-based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and response generating program 175.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The invention may be beneficially practiced in any system, single orparallel, which processes an instruction stream. The computer programproduct may include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, or computer readable storage device,as used herein, is not to be construed as being transitory signals perse, such as radio waves or other freely propagating electromagneticwaves, electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions collectively stored thereincomprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

References in the specification to “one embodiment”, “an embodiment”,“an example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A method for generating a query response, themethod comprising: receiving, by one or more computer processors, datafor a non-user utterance; determining, by the one or more computerprocessors, a question answering system response to the non-userutterance; receiving, by the one or more computer processors, data for auser utterance responsive to the non-user utterance; determining, by theone or more computer processors, a confidence score for the userutterance; determining, by the one or more computer processors, adeviation between the user utterance and the question answering systemresponse; and providing, by the one or more computer processors, thequestion answering system response according to a combination of thedeviation and the confidence score.
 2. The method according to claim 1,further comprising training, by the one or more computer processors, thequestion answering system by: receiving user biometric data; andbuilding a knowledge graph incorporating the user biometric data.
 3. Themethod according to claim 1, further comprising providing, by the one ormore computer processors, the question answering system response to thenon-user.
 4. The method according to claim 1, wherein determining theconfidence score includes consideration of an attribute selected fromthe group consisting of pause gap analysis, tonal analysis, facialexpression analysis, personality trait analysis, and combinationsthereof.
 5. The method according to claim 1, further comprisingproviding, by the one or more computer processors, the questionanswering system response to the user according to a responsesensitivity.
 6. The method according to claim 1, further comprisingproviding, by the one or more computer processors, the questionanswering system response according to a user opt-in status.
 7. Themethod according to claim 1, further comprising providing, by the one ormore computer processors, the question answering system responseaccording to a communication context factor.
 8. A computer programproduct for generating a query response, the computer program productcomprising one or more computer readable storage devices andcollectively stored program instructions on the one or more computerreadable storage devices, the stored program instructions comprising:program instructions to receive data for a non-user utterance; programinstructions to determine a question answering system response to thenon-user utterance; program instructions to receive data for a userutterance responsive to the non-user utterance; program instructions todetermine a confidence score for the user utterance; programinstructions to determine a deviation between the user utterance and thequestion answering system response; and program instructions to providethe question answering system response according to a combination of thedeviation and the confidence score.
 9. The computer program productaccording to claim 8, the stored program instructions further comprisingprogram instructions to train the question answering system by:receiving user biometric data; and building a knowledge graphincorporating the user biometric data.
 10. The computer program productaccording to claim 8, the stored program instructions further comprisingprogram instructions to provide the question answering system responseto the non-user.
 11. The computer program product according to claim 8,wherein determining the confidence score includes consideration of anattribute selected from the group consisting of pause gap analysis,tonal analysis, facial expression analysis, personality trait analysis,and combinations thereof.
 12. The computer program product according toclaim 8, the stored program instructions further comprising programinstructions to provide the question answering system response to theuser according to a response sensitivity.
 13. The computer programproduct according to claim 8, the stored program instructions furthercomprising program instructions to provide the question answering systemresponse according to a user opt-in status.
 14. The computer programproduct according to claim 8, the stored program instructions furthercomprising program instructions to provide the question answering systemresponse according to a communication context factor.
 15. A computersystem for generating a query response, the computer system comprising:one or more computer processors; one or more computer readable storagedevices; and stored program instructions on the one or more computerreadable storage devices for execution by the one or more computerprocessors, the stored program instructions comprising: programinstructions to receive data for a non-user utterance; programinstructions to determine a question answering system response to thenon-user utterance; program instructions to receive data for a userutterance responsive to the non-user utterance; program instructions todetermine a confidence score for the user utterance; programinstructions to determine a deviation between the user utterance and thequestion answering system response; and program instructions to providethe question answering system response according to a combination of thedeviation and the confidence score.
 16. The computer system according toclaim 15, the stored program instructions further comprising programinstructions to train the question answering system by: receiving userbiometric data; and building a knowledge graph incorporating the userbiometric data.
 17. The computer system according to claim 15, thestored program instructions further comprising program instructions toprovide the question answering system response to the non-user.
 18. Thecomputer system according to claim 15, wherein determining theconfidence score includes consideration of an attribute selected fromthe group consisting of pause gap analysis, tonal analysis, facialexpression analysis, personality trait analysis, and combinationsthereof.
 19. The computer system according to claim 15, the storedprogram instructions further comprising program instructions to providethe question answering system response to the user according to aresponse sensitivity.
 20. The computer system according to claim 15, thestored program instructions further comprising program instructions toprovide the question answering system response according to a useropt-in status.